{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Ch05: Magnetohydrodynamics\n",
    "# *Plasma as a fluid*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We now focus on the plasma as a single fluid. This mean averaging the electron motion over large spatial and time scales so the electron physics can be encapsulated readily into the ion flow. The trick is to retain as much as possible of the electron physics. But not too much, or the code will have to resolve electron effects which are much smaller and much faster than the ion fluid. \n",
    "\n",
    "This would reduce the numerical time step in our discrete equations, making our code very slow. This chapter unite both ion and electron physics via a generalized Ohm's law and the global electromagnetic fields, as done by Vlasov for kinetic theory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step in getting the single fluid approximation is to sum up all the quantities evolved by the fluid equations to get\n",
    "$$\\begin{align}\n",
    "&n=\\sum_{i,e}n_{i,e}\\tag{5.1}\\\\\n",
    "&\\rho=\\sum_{i,e}n_{i,e}m_{i,e}\\tag{5.2}\\\\\n",
    "&n\\vec u=\\frac{1}{\\rho}\\sum_{i,e}\\rho_{i,e}u_{i,e}\\tag{5.3}\\\\\n",
    "&\\vec j=Z_ien_i\\vec u_i-en_e\\vec u_e\\tag{5.4}\\end{align}$$\n",
    "From now on, we will call $\\vec u$ the bulk flow of the fluid. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Generalized Ohm's law\n",
    "We now go beyond the simple understanding that electric field can generate only electrical currents inside a plasma, or vise versa,i.e.\n",
    "$$\\vec E=\\eta \\vec j$$\n",
    "In fact, we have seen that there is only a relashionship between currents and electric field through resistivity. When the resistivity $\\eta$ is simply 0, there is no electric field associated with electrical currents. But there are other cause for electric field to exists inside a plasma:\n",
    "1. Dynamo\n",
    "2. Hall effect\n",
    "3. Electron pressure (also known as the Biermann battery term)\n",
    "4. Electron inertia\n",
    "\n",
    "Let's look at each of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Dynamo and the Hall effect\n",
    "This electric field is created by the drift of electron fluid compare to the ion fluid caused by a magnetic field. For one particle we know that $ \\vec F=q\\vec E+q\\vec v\\times\\vec B$. if we suppose that the electron fluid is tied to the ion fluid then the global force on the electron fluid statistically balance the local force $\\vec F$ so we get $q\\vec E+q\\vec u_e\\times\\vec B=\\vec 0$ which give the first electric field to take into account inside plasma:\n",
    "$$\\vec E=-\\vec u_e\\times\\vec B\\tag{5.5}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now we work on the single fluid approximation of plasma so we need to get rid of $\\vec u_e$. The global current density is given by $$\\vec j=Z_ien_i\\vec u_i-en_e\\vec u_e\\tag{5.6}$$\n",
    "Using quasi-neutrality, we can rewrite $\\vec u_e$ as\n",
    "$$u_e=\\vec u_i-\\frac{1}{en_e}\\vec j$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Since the ion mass $m_i$ is much larger than the electron mass $m_e$\n",
    "$$\\vec u_i=\\frac{\\rho\\vec u+m_e\\vec j/e}{\\rho}\\approx\\vec u\\tag{5.7}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We end up with $$\\vec E=-\\vec u\\times\\vec B+\\frac{1}{en_e}\\vec j\\times\\vec B\\tag{5.8}$$\n",
    "The first term on the right hand side the the dynamo term. A conductive flow moving inside a magnetic field creates an electric field, call the dynamo field, after Michael Faraday who coined the term for a machine capable of generating electric fields from flowing conductive fluid inside a magnetic field. The second term is the Hall effect, named after Edwin Hall, who discovered that electric field are generated when current flows inside a magnetic field. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Eq. $(5.8)$ shows that the electric field in the frame of reference of the flowing plasma is not $\\vec E$. It is in fact $\\vec {E}^*$, given by\n",
    "$$\\vec E\\,^*=\\vec E+\\vec u\\times\\vec B\\tag{5.9}$$\n",
    "So, this state of affair requires us to rewrite the Hall effect as\n",
    "$$\\vec E\\,^*=\\frac{1}{en_e}\\vec j\\times\\vec B\\tag{5.10}$$\n",
    "and, of course, Ohm's law as\n",
    "$$\\vec E\\,^*=\\eta\\vec j\\tag{5.11}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Electron pressure (a.k.a. the Biermann battery)\n",
    "Electric fields can also create a pressure gradient of electron. if there is more electron at one location inside the plasma than at another, then there is an electrostatic field building up. Normally, the electric field should be able to restore electrostatic balance inside the plasma. What happens if it can't? Typically this situation arises when the thermal energy of the electrons is large enough to fight the electric field. In this case, the electric force density $\\vec f_E=-en_e\\vec E\\,^*$ is balanced exactly by the pressure gradient caused by the difference in electron density $\\vec f_{\\nabla p}=\\vec \\nabla p_e$. Since we are at equilibrium we get \n",
    "$$\\vec E\\,^*=-\\frac{1}{en_e}\\vec \\nabla p_e\\tag{5.12}$$\n",
    "This situation can also happen when there is a spacial difference in electron temperature between two regions. The region where the temperature is larger pushes against the region where the temperature is smaller, again generating a pressure gradient. This gradient can exist only if there is an electric field that can balance it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This effect is of particular interest in astrophysics since it can generate magnetic fields. Starting from the induction equation $$\\frac{\\partial \\vec B}{\\partial t}=-\\vec \\nabla\\times\\vec E\\tag{5.13}$$ and using the fact that $p_e=en_eT_e$. Note that we used $e$ here because the temperature $T_e$ is in $V$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    " we get $$\\vec \\nabla p_e=eT_e\\vec \\nabla n_e+en_e\\vec\\nabla T_e\\tag{5.14}$$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Using Eq. $(5.13)$ and $(5.14)$ we obtain the following equation\n",
    "$$\\frac{\\partial \\vec B}{\\partial t}=\\vec \\nabla\\times\\Big(\\frac{T_e}{n_e}\\vec \\nabla n_e+\\vec\\nabla T_e\\Big)\\tag{5.15}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We know that $\\vec\\nabla\\times(\\vec\\nabla \\varphi)=\\vec 0$ and $\\vec\\nabla\\times(\\varphi\\vec A)=\\varphi(\\vec\\nabla\\times\\vec A)+(\\vec\\nabla \\varphi)\\times\\vec A$. So we can simplify Eq. $(5.15)$\n",
    "$$\\frac{\\partial \\vec B}{\\partial t}=\\vec \\nabla\\Big(\\frac{T_e}{n_e}\\Big)\\times\\vec \\nabla n_e$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "and rewrite it as\n",
    "$$\\frac{\\partial \\vec B}{\\partial t}=\\Big(\\frac{\\vec \\nabla T_e}{n_e}-\\frac{T_e\\vec \\nabla n_e}{n_e^2}\\Big)\\times\\vec \\nabla n_e$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We finally get the the Biermann battery term\n",
    "$$\\frac{\\partial \\vec B}{\\partial t}=\\frac{1}{en_e}\\vec \\nabla (eT_e)\\times\\vec \\nabla n_e\\tag{5.16}$$\n",
    "So magnetic fields are generated from gradients in density and temperature with perpendicular components. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Electron Inertia\n",
    "We mentioned earlier that the electric field seen by charged particles inside a flow is not $\\vec E$ but $\\vec E\\,^*$. This electric field will generate a pull on all charged particles. But the electrons are much lighter than the ions. Consequently, the action on the electron is most noticeable. If we ignore effects on the ions, we get $$-e\\vec E\\,^*=m_e\\frac{\\partial(\\vec v_e-\\vec u)}{\\partial t}.$$ We had to remove the speed $\\vec u$ of the fluid here since we are using the electric field in the frame of reference of the plasma $\\vec E\\,^*$. We could rewrite this equation as\n",
    "$$e\\vec E\\,^*=\\frac{m_e}{en_e}\\frac{\\partial \\vec j}{\\partial t}\\tag{5.17}.$$\n",
    "However, this approach is not completely correct. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To use $\\vec j$ we have turned the speed of the individual electrons $\\vec v_e$ into the flow speed of the electron flow $\\vec u_e$. Yet, we would like Eq. $(5.17)$ to be an expression of conservation of momentum for the electron flow inside the bulk flow. So we can simply rewrite it as a conservation equation by adding the bulk flow convective term $\\nabla(\\vec j\\vec u)$ (i.e. $\\vec j$ is advected by $\\vec u$). Yet, the flow of electron does also advect its own momentum and this is where it becomes slightly more complicated and we just give the final answer here\n",
    "$$\\vec E\\,^*=\\frac{m_e}{e^2n_e}\\Big(\\frac{\\partial \\vec j}{\\partial t}+\\vec\\nabla\\cdot\\big[\\vec u\\,\\vec j+\\vec j\\,\\vec u+\\frac{1}{en_e}\\vec j\\,\\vec j\\big]\\Big)\\tag{5.18}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The equations of extended magnetohydrodynamics\n",
    "Extended magnetohydrodynamics is a particular version of magnetohydrodynamics where only low frequency electron physics is kept. While it does not capture microscopic electron physics like plasma oscillations, it capture most of the electron physics that is expressed on the ion spatial and time scales. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can now gather all the electric field terms from Eqs. $(5.10)$, $(5.11)$, $(5.12)$ and $(5.18)$ together to get the total electric field $\\vec E\\,^*$ inside the bulk flow is\n",
    "$$\\vec E\\,^*=\\frac{1}{en_e}\\vec j\\times\\vec B+\\eta\\vec j-\\frac{1}{en_e}\\vec \\nabla p_e+\\frac{m_e}{e^2n_e}\\Big(\\frac{\\partial \\vec j}{\\partial t}+\\vec\\nabla\\cdot\\big[\\vec u\\,\\vec j+\\vec j\\,\\vec u+\\frac{1}{en_e}\\vec j\\,\\vec j\\big]\\Big)$$\n",
    "As a result, the electric field as seen by the plasma in the rest frame is\n",
    "$$\\vec E=-\\vec u\\times\\vec B+\\eta\\vec j+\\frac{1}{en_e}\\big(\\vec j\\times\\vec B-\\vec \\nabla p_e\\big)+\\frac{m_e}{e^2n_e}\\Big(\\frac{\\partial \\vec j}{\\partial t}+\\vec\\nabla\\cdot\\big[\\vec u\\,\\vec j+\\vec j\\,\\vec u+\\frac{1}{en_e}\\vec j\\,\\vec j\\big]\\Big)\\tag{5.19}$$\n",
    "This term corresponds to the time it takes for the electrons to respond to changes in electric fields due to their mass."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If you remember Ampere's laws\n",
    "$$\\frac{\\partial\\vec {E}}{\\partial t}=\\frac{1}{\\mu_0\\varepsilon_0}\\vec \\nabla\\times\\vec {B}-\\frac{1}{\\varepsilon_0}\\vec {j}\\tag{5.20}$$\n",
    "and Faraday's law\n",
    "$$\\frac{\\partial\\vec {B}}{\\partial t}=-\\vec \\nabla\\times\\vec {E}\\tag{5.21}$$\n",
    "from the previous chapter we might be able to see a little problem arising."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "While it can be shown that $\\vec E$ can be a solution of Eq. $(5.20)$ while satisfying the constraint of Maxwell's equations given in $(4.7)$, there is no mathematical proof that $\\vec E$ can satisfy simultaneously Eq. $(5.19)$ and $(5.20)$. Well this might be problematic. Now we have two options. The first is to ignore radiations. So we can rewrite Eq. $(5.20)$ as\n",
    "$$\\vec \\nabla\\times\\vec {B}=\\mu_0\\vec {j}\\tag{5.22}$$\n",
    "In this case all is well as long as we enforce that $\\vec\\nabla\\cdot \\vec B=0$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The other possibility is to write Eq. $(5.19)$ in its original form, that is\n",
    "$$\\frac{\\partial \\vec j}{\\partial t}+\\vec\\nabla\\cdot\\big[\\vec u\\,\\vec j+\\vec j\\,\\vec u+\\frac{1}{en_e}\\vec j\\,\\vec j\\big]=\\frac{e^2n_e}{m_e}\\Big(\\vec E+\\vec u\\times\\vec B-\\eta\\vec j-\\frac{1}{en_e}[\\,\\vec j\\times\\vec B-\\vec \\nabla p_e]\\Big)\\tag{5.23}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now we have obtained a time advanced equation for the current density $\\vec j$. Yet this equation is still on the electron time scale and, if solved directly, will suffer from a very small CFL conditions. However, it is possible to solve it implicitly when the value $e^2n_e/m_e$ is relatively large ($n_e>10^{10}m^{-3}$). In this case, we can force the right hand side of Eq. $(5.23)$ to be 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "At this point, we can first solve Maxwell's equations using\n",
    "$$\\begin{align}\n",
    "&\\frac{\\partial\\vec {E}}{\\partial t}=\\frac{1}{\\mu_0\\varepsilon_0}\\vec \\nabla\\times\\vec {B}-\\frac{1}{\\varepsilon_0}\\vec {j}\\\\\n",
    "&\\frac{\\partial\\vec {B}}{\\partial t}=-\\vec \\nabla\\times\\vec {E}\\end{align}$$\n",
    "together with\n",
    "$$\\frac{\\partial \\vec j}{\\partial t}+\\vec\\nabla\\cdot\\big[\\vec u\\,\\vec j+\\vec j\\,\\vec u+\\frac{1}{en_e}\\vec j\\,\\vec j\\big]=0\\tag{5.24}$$\n",
    "on the ion time scale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Then, we correct the values of $\\vec E$ and $\\vec j$ using\n",
    "$$\\vec E+\\vec u\\times\\vec B-\\eta\\vec j-\\frac{1}{en_e}[\\,\\vec j\\times\\vec B-\\vec \\nabla p_e]=0\\tag{5.25}$$\n",
    "At this point, we have implicitly found the contribution of electron physics *at the ion time scale* using the generalized Ohm's law, without the electron inertia term. One important property of Eq. $(5.25)$ is its lack of derivatives. The coupling between $E$ and $j$ forms a linear set of equations, which matrix is only $3\\times3$ and can be inverted analytically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Using the form of energy density defined in Ch04\n",
    "$$m_i\\epsilon=\\frac{3}{2}p+\\frac{1}{2}\\rho\\vec u^2\\tag{5.26}$$\n",
    "where $m_i$ is the ion mass,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "our final set of equations for extended magnetohydrodynamics are\n",
    "$$\\begin{align}\n",
    "&\\frac{\\partial n}{\\partial t}+\\vec \\nabla . \\big(n\\vec u\\big)=0\\\\\n",
    "&\\frac{\\partial n\\vec u}{\\partial t}+\\vec \\nabla . \\big(n\\vec u\\vec u\\big)=\\vec j\\times\\vec B-\\vec\\nabla\\cdot p\\\\\n",
    "&\\frac{\\partial \\epsilon}{\\partial t}+\\vec \\nabla \\cdot (\\epsilon\\vec u)=\\frac{1}{m}\\big(-\\vec \\nabla \\cdot Q+\\vec E\\cdot\\vec j\\big)\\\\\n",
    "&\\frac{\\partial\\vec {E}}{\\partial t}=\\frac{1}{\\mu_0\\varepsilon_0}\\vec \\nabla\\times\\vec {B}-\\frac{1}{\\varepsilon_0}\\vec {j}\\\\\n",
    "&\\frac{\\partial\\vec {B}}{\\partial t}=-\\vec \\nabla\\times\\vec {E}\\\\\n",
    "&\\frac{\\partial \\vec j}{\\partial t}+\\vec\\nabla\\cdot\\big[\\vec u\\,\\vec j+\\vec j\\,\\vec u+\\frac{1}{en_e}\\vec j\\,\\vec j\\big]=0\\\\\n",
    "&\\vec E+\\vec u\\times\\vec B-\\eta\\vec j-\\frac{1}{en_e}[\\,\\vec j\\times\\vec B-\\vec \\nabla p_e]=0\n",
    "\\end{align}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Resistive magnetohydrodynamics\n",
    "If the value $e^2n_e/m_e$ is too small ($n_e<<10^{10}m^{-3}$) then the model presented above is not strictly correct. While it can still be used, it may not be possible to obtain a physical solution. If electron physics is important then, we need to use the two-fluid magnetohydrodynamics equations discussed in the previous chapter. However, if electron physics cna be neglected, then we can drop Eq. $(5.25)$ and simplify Eq. $(5.24)$ to a trimmed down version of the generalized Ohm's law\n",
    "$$\\vec E=-\\vec u\\times\\vec B+\\eta\\vec j\\tag{5.27}$$\n",
    "\n",
    "Since we cannot that the electric field verifies both Maxwell's equations and Ohm's law, because we lost the degrees of freedom provided bu the time evolution of the current density, we need to make so further concessions and drop electromagnetic waves, i.e. $\\partial\\vec E/\\partial t=\\vec 0$. At this point the current density is fully defined by\n",
    "$$\\vec j=\\frac{1}{\\mu_0}\\vec\\nabla\\times\\vec B\\tag{5.28}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The other equations remain unchanged after we have replaced both $\\vec E$ and $\\vec j$ by their values given by Ohm's law and ampere's law.\n",
    "$$\\begin{align}\n",
    "&\\frac{\\partial n}{\\partial t}+\\vec \\nabla . \\big(n\\vec u\\big)=0\\\\\n",
    "&\\frac{\\partial n\\vec u}{\\partial t}+\\vec \\nabla . \\big(n\\vec u\\vec u\\big)=\\frac{1}{\\mu_0}\\big(\\vec\\nabla\\times\\vec B\\big)\\times\\vec B-\\vec\\nabla\\cdot p\\\\\n",
    "&\\frac{\\partial \\epsilon}{\\partial t}+\\vec \\nabla \\cdot (\\epsilon\\vec u)=\\frac{1}{m}\\Big(-\\vec \\nabla \\cdot Q+\\frac{1}{\\mu_0}\\big(\\vec\\nabla\\times\\vec B\\big)\\cdot\\big[\\frac{\\eta}{\\mu_0}\\vec\\nabla\\times\\vec B-\\vec u\\times\\vec B\\big]\\Big)\\\\\n",
    "&\\frac{\\partial\\vec {B}}{\\partial t}=-\\vec \\nabla\\times\\big(\\frac{\\eta}{\\mu_0}\\vec\\nabla\\times\\vec B-\\vec u\\times\\vec B\\big)\\\\\n",
    "&\\vec \\nabla\\cdot\\vec B=0\\\\\n",
    "\\end{align}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Here both $\\vec E$ and $\\vec j$ have disappeared from the equations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Ideal magnetohydrodynamics\n",
    "Ideal magnetohydrodynamics suppose the resistivity $\\eta$ negligible compared to dynamo. And we are left with \n",
    "$$\\begin{align}\n",
    "&\\frac{\\partial n}{\\partial t}+\\vec \\nabla . \\big(n\\vec u\\big)=0\\\\\n",
    "&\\frac{\\partial n\\vec u}{\\partial t}+\\vec \\nabla . \\big(n\\vec u\\vec u\\big)=\\frac{1}{\\mu_0}\\big(\\vec\\nabla\\times\\vec B\\big)\\times\\vec B-\\vec\\nabla\\cdot p\\\\\n",
    "&\\frac{\\partial \\epsilon}{\\partial t}+\\vec \\nabla \\cdot (\\epsilon\\vec u)=-\\frac{\\vec \\nabla \\cdot Q}{m}\\\\\n",
    "&\\frac{\\partial\\vec {B}}{\\partial t}=\\vec \\nabla\\times\\big(\\vec u\\times\\vec B\\big)\\\\\n",
    "&\\vec\\nabla\\cdot\\vec B=0\n",
    "\\end{align}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Extended magnetohydrodynamics code\n",
    "It is now possible to construct an extended magnetohydrodynamics code using the numerical method presented in Ch04 and the equations of extended magnetohydrodynamics. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Python housekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "from math import sqrt\n",
    "import matplotlib \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "from numba import jit\n",
    "from ipywidgets.widgets.interaction import show_inline_matplotlib_plots\n",
    "from IPython.display import clear_output\n",
    "from ipywidgets import FloatProgress\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Definitions and constants\n",
    "as before, we define some constants that will be use to remember which component in the array correspond to which quantity, e.g. the temperature can be accessed using `tp` at the location `i,j` as `Q[tp,i,j]`. `rh` is the number density, `mx` is the momentum density in the x-direction, `my` and `mz` in the y and z directions. `en` is for the energy density. We also introduce new variables:\n",
    "1. the magnetic field constants: `bx`, `by`, `bz`\n",
    "2. the electric field constants: `ex`, `ey` and `ez`\n",
    "3. the current density constants: `jx`, `jy` and `jz`\n",
    "4. the energy density for the electrons: `et`\n",
    "6. the electron density: `ne`\n",
    "7. the electron pressure: `ep`\n",
    "8. the extended magneto hydrodynamics resistivity: `etax`\n",
    "9. the electron and ion temperatures, `tpe` and `tpi` respectively\n",
    "`nQ` is the total number of variables, from `rh` to `tp`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "rh=0\n",
    "mx=1\n",
    "my=2\n",
    "mz=3\n",
    "en=4\n",
    "bx=5\n",
    "by=6\n",
    "bz=7\n",
    "ex=8\n",
    "ey=9\n",
    "ez=10\n",
    "jx=11\n",
    "jy=12\n",
    "jz=13\n",
    "et=14\n",
    "ne=15\n",
    "ep=16\n",
    "etax=17\n",
    "tpi=18\n",
    "tpe=19\n",
    "nQ=20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`mu` is the atomic number of the fluid, in this case aluminum. `aindex` is the adiabatic index. `Z` is the average ionization factor and `er2` is used to reduce the speed of light, so computations run faster. While this is physically incorrect to reduce the speed of light, it does nto cause problems as long as the fastest speeds inside the plasmas are much smaller than the speed of light."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "Z=4.\n",
    "mu=27.\n",
    "er2=100.\n",
    "aindex=1.1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Here we define our dimensional parameters. `L0` is the geometrical scale length, `t0` the time scale, `n0` the density scale, `v0` is the velocity scale, `p0` the pressure scale, `te0` the temperature scale.  We also define a couple of floor quantities under which no density, temperature and pressure can go. Then we define all our electromagnetic related constants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "L0=1.0e-3\n",
    "t0=100.0e-9\n",
    "n0=6.0e28\n",
    "v0=L0/t0\n",
    "p0=mu*1.67e-27*n0*v0**2\n",
    "te0=p0/n0/1.6e-19\n",
    "rh_floor = 1.0E-8\n",
    "T_floor = 0.026/te0\n",
    "rh_mult = 1.1\n",
    "P_floor = rh_floor*T_floor\n",
    "\n",
    "mu0=4.e-7*np.pi\n",
    "eps0=8.85e-12\n",
    "mime=mu*1836.\n",
    "memi=1./mime\n",
    "\n",
    "B0=math.sqrt(mu*1.67e-27*mu0*n0)*v0\n",
    "E0=v0*B0\n",
    "J0=B0/(mu0*L0)\n",
    "eta0=mu0*v0*L0\n",
    "cflm = 0.8\n",
    "gma2 = 2.8e-8*mu0*L0**2*n0\n",
    "lil0 = 2.6e5*math.sqrt(mu/n0/mu0)/L0\n",
    "cab = t0/L0/math.sqrt(eps0*mu0)\n",
    "ecyc = 1.76e11*B0*t0\n",
    "clt = cab/er2\n",
    "cfva2=(cab/er2)**2\n",
    "vhcf = 1.0e6\n",
    "sigma = 1.2e2\n",
    "taui0 = 6.2e14*sqrt(mu)/n0\n",
    "omi0 = 9.6e7*Z*B0/mu\n",
    "bapp = 0.\n",
    "visc0 = 5.3*t0\n",
    "ome0 = mime*omi0\n",
    "taue0 = 61.*taui0 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We now define our geometrical functions. They are used quite often so we will compile them inside the code to accelerate computations. To avoid writing two codes, one for slab symmetry and one for cylindrical symmetry, we wrote this code in for slab symmetry and in Cartesian coordinates, i.e. $(x,y,z)$. When using the code for problem with cylindrical symmetry then $$\\begin{align}\n",
    "&x\\rightarrow r\\\\\n",
    "&y\\rightarrow z\\\\\n",
    "&z\\rightarrow \\phi\\end{align}$$\n",
    "\n",
    "The `rc` function computes the radius in cylindrical coordinates, which appears in source terms and in the conservation equations. Clearly, this is simply $x$ as mentioned above. That's why `rc` returns `xc`. To turn the code into slab symmetry, just have the function `rc` return `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def xc(i): \n",
    "    return loc_lxd + (i-ngu+1)/dxi\n",
    "\n",
    "@jit(nopython=True)\n",
    "def rc(i):\n",
    "    return xc(i)\n",
    "\n",
    "@jit(nopython=True)\n",
    "def yc(j):\n",
    "    return loc_lyd + (j-ngu+1)/dyi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We now add radius and angle function for axi-symmetric distribution in slab geometries, not ot be confused with `rc` above. Note the the letter `c`, for center, is now gone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def r(i,j):\n",
    "    return math.sqrt((xc(i)-(lxd+lxu)/2)**2 + (yc(j)-(lyd+lyu)/2)**2)\n",
    "\n",
    "@jit(nopython=True)\n",
    "def rz(i,j):\n",
    "    return sqrt(xc(i)**2+yc(j)**2)\n",
    "\n",
    "@jit(nopython=True)\n",
    "def theta(i,j):\n",
    "    return math.atan(yc(j)-(lyd+lyu)/2,xc(i)-(lxd+lxu)/2)\n",
    "  \n",
    "@jit(nopython=True)\n",
    "def Icur(t):\n",
    "    return Ipeak*math.sin(np.pi*t/(2*tr))*math.exp(-t/tdp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Finite volume method and time advance algorithms\n",
    "We now compute the sources for all our conservation equations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def get_sources(Qin):\n",
    "    sourcesin=np.zeros((nx,ny,nQ))\n",
    "    nuei=np.empty((nx,ny))\n",
    "    gyro = memi*ecyc\n",
    "    linv = 1./lil0\n",
    "    fac = lil0/(mime + Z)\n",
    "    Zi = 1./Z\n",
    "    Zin = 1./(Z + 1.)\n",
    "    dti = 0.2/dt\n",
    "\n",
    "    dx2 = 1./(dt*dxi**2)\n",
    "    dx = 1./dxi\n",
    "    mui = (1. + Z)*t0\n",
    "    t0i = 1./t0\n",
    "    kap_e = 0.\n",
    "    kap_i = 0.\n",
    "\n",
    "    for j in range(ngu, ny-ngu):\n",
    "        for i in range(ngu, nx-ngu):\n",
    "\n",
    "            rbp = 0.5*(rc(i+1) + rc(i))\n",
    "            rbm = 0.5*(rc(i) + rc(i-1))\n",
    "\n",
    "            dni = Qin[i,j,rh]\n",
    "            dne = Qin[i,j,ne]\n",
    "            dnii = 1./dni\n",
    "            dnei = 1./dne\n",
    "\n",
    "            vix = Qin[i,j,mx]*dnii\n",
    "            viy = Qin[i,j,my]*dnii\n",
    "            viz = Qin[i,j,mz]*dnii \n",
    "\n",
    "            vex = vix - lil0*Qin[i,j,jx]*dnei\n",
    "            vey = viy - lil0*Qin[i,j,jy]*dnei\n",
    "            vez = viz - lil0*Qin[i,j,jz]*dnei  \n",
    "\n",
    "            Qin[i,j,tpi] = (aindex - 1)*(Qin[i,j,en]*dnii - 0.5*(vix**2 + viy**2 + viz**2))\n",
    "            if(Qin[i,j,tpi]  <  T_floor):\n",
    "                Qin[i,j,tpi] = T_floor\n",
    "\n",
    "            Qin[i,j,tpe] = (aindex - 1)*(Qin[i,j,et]*dnei - 0.5*memi*(vex**2 + vey**2 + vez**2))  \n",
    "            if(Qin[i,j,tpe]  <  T_floor):\n",
    "                Qin[i,j,tpe] = T_floor \n",
    "\n",
    "            eta_s = 0.5*Z/(Zin* Qin[i,j,tpi])**1.5\n",
    "            Qin[i,j,etax] = 1./sqrt(1./eta_s**2 + 1.e6*dni**2) \n",
    "            nuei[i,j] = gma2*dne*Qin[i,j,etax]\n",
    "\n",
    "            sourcesin[i,j,ne] = dti*(Z*Qin[i,j,rh] - Qin[i,j,ne])\n",
    "\n",
    "            sourcesin[i,j,mx] = Qin[i,j,jy]*Qin[i,j,bz] - Qin[i,j,jz]*Qin[i,j,by]\n",
    "            sourcesin[i,j,my] = Qin[i,j,jz]*Qin[i,j,bx] - Qin[i,j,jx]*Qin[i,j,bz]\n",
    "            sourcesin[i,j,mz] = Qin[i,j,jx]*Qin[i,j,by] - Qin[i,j,jy]*Qin[i,j,bx]\n",
    "\n",
    "            ux = vix\n",
    "            uy = viy\n",
    "            uz = viz\n",
    "            P = dni*Qin[i,j,tpi] + dne*Qin[i,j,tpe]\n",
    "            \n",
    "\n",
    "            sourcesin[i,j,en] = Z*gyro*dni*(vix*Qin[i,j,ex] + viy*Qin[i,j,ey] + viz*Qin[i,j,ez])- memi*lil0*nuei[i,j]*(vix*Qin[i,j,jx] + viy*Qin[i,j,jy] + viz*Qin[i,j,jz])+ 3*memi*nuei[i,j]*dni*(Qin[i,j,tpe] - Qin[i,j,tpi])\n",
    "\n",
    "            sourcesin[i,j,et] =  -gyro*dne*(vex*Qin[i,j,ex] + vey*Qin[i,j,ey] + vez*Qin[i,j,ez])+ memi*lil0*nuei[i,j]*(vix*Qin[i,j,jx] + viy*Qin[i,j,jy] + viz*Qin[i,j,jz]) - 3*memi*nuei[i,j]*dni*(Qin[i,j,tpe] - Qin[i,j,tpi])  \n",
    "\n",
    "            Qin[i,j,ep] = dne*Qin[i,j,tpe]\n",
    " \n",
    "            sourcesin[i,j,ne] = sourcesin[i,j,ne]*rc(i)\n",
    "            sourcesin[i,j,mx] = sourcesin[i,j,mx]*rc(i) + (Qin[i,j,mz]*viz + P)\n",
    "            sourcesin[i,j,mz] = sourcesin[i,j,mz]*rc(i) - Qin[i,j,mz]*vix\n",
    "            sourcesin[i,j,en] = sourcesin[i,j,en]*rc(i)\n",
    "            sourcesin[i,j,et] = sourcesin[i,j,et]*rc(i)\n",
    "            sourcesin[i,j,jx] =  (uz*Qin[i,j,jz] + Qin[i,j,jz]*uz - lil0*dnei*Qin[i,j,jz]*Qin[i,j,jz])\n",
    "            sourcesin[i,j,jz] = -(ux*Qin[i,j,jz] + Qin[i,j,jx]*uz - lil0*dnei*Qin[i,j,jz]*Qin[i,j,jx])\n",
    "            sourcesin[i,j,bz] = Qin[i,j,ey]\n",
    "            sourcesin[i,j,ez] = -cfva2*Qin[i,j,by]\n",
    "    return sourcesin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We compute the electric field using constrained transport, to make sure that $\\vec\\nabla\\cdot\\vec B=0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def get_ect(flux_x,flux_y):\n",
    "    ect=np.zeros((nx,ny))\n",
    "    for j  in range(ngu-1, ny-ngu+1):\n",
    "        for i  in range(ngu-1, nx-ngu):\n",
    "            ect[i,j] = 0.25*(-flux_x[i-1,j,by] - flux_x[i,j,by] + flux_y[i,j-1,bx] + flux_y[i,j,bx])\n",
    "    #ect[:,1] = ect[:,2]\n",
    "    #ect[1,:] = -ect[2,:]\n",
    "    return ect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here do the time advance of the conservation equations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def advance_time_level(Qin,ect,flux_x,flux_y,source):\n",
    "    Qout=np.zeros((nx,ny,nQ))\n",
    "    fac = 0*mime/lil0\n",
    "\n",
    "    dxt = dxi*dt\n",
    "    dyt = dyi*dt\n",
    "\n",
    "    ect=get_ect(flux_x,flux_y)\n",
    "\n",
    "    for j  in range(ngu, ny-ngu):\n",
    "        for i  in range(ngu, nx-ngu):\n",
    "            rbp = 0.5*(rc(i+1) + rc(i))\n",
    "            rbm = 0.5*(rc(i) + rc(i-1))\n",
    "            rci = 1./rc(i)\n",
    "\n",
    "            Qout[i,j,rh] = Qin[i,j,rh]*rc(i) - dxt*(flux_x[i,j,rh]*rbp-flux_x[i-1,j,rh]*rbm) - dyt*(flux_y[i,j,rh]-flux_y[i,j-1,rh])*rc(i) \n",
    "            Qout[i,j,mx] = Qin[i,j,mx]*rc(i) - dxt*(flux_x[i,j,mx]*rbp-flux_x[i-1,j,mx]*rbm) - dyt*(flux_y[i,j,mx]-flux_y[i,j-1,mx])*rc(i) + dt*source[i,j,mx] \n",
    "            Qout[i,j,my] = Qin[i,j,my]*rc(i) - dxt*(flux_x[i,j,my]*rbp-flux_x[i-1,j,my]*rbm) - dyt*(flux_y[i,j,my]-flux_y[i,j-1,my])*rc(i) + dt*source[i,j,my] \n",
    "            Qout[i,j,mz] = Qin[i,j,mz]*rc(i) - dxt*(flux_x[i,j,mz]*rbp-flux_x[i-1,j,mz]*rbm) - dyt*(flux_y[i,j,mz]-flux_y[i,j-1,mz])*rc(i) + dt*source[i,j,mz]\n",
    "            Qout[i,j,en] = Qin[i,j,en]*rc(i) - dxt*(flux_x[i,j,en]*rbp-flux_x[i-1,j,en]*rbm) - dyt*(flux_y[i,j,en]-flux_y[i,j-1,en])*rc(i) + dt*source[i,j,en]\n",
    "\n",
    "            Qout[i,j,bx] = Qin[i,j,bx]*rc(i) - 0.5*dyt*(ect[i,j+1]*rc(i) - ect[i,j-1]*rc(i))\n",
    "            Qout[i,j,by] = Qin[i,j,by]*rc(i) + 0.5*dxt*(ect[i+1,j]*rc(i+1) - ect[i-1,j]*rc(i-1))\n",
    "            Qout[i,j,bz] = Qin[i,j,bz]*rc(i) - dxt*(flux_x[i,j,bz]*rbp-flux_x[i-1,j,bz]*rbm) - dyt*(flux_y[i,j,bz]-flux_y[i,j-1,bz])*rc(i) + dt*source[i,j,bz]\n",
    "\n",
    "            Qout[i,j,ex] = Qin[i,j,ex]*rc(i) - dxt*(flux_x[i,j,ex]*rbp-flux_x[i-1,j,ex]*rbm) - dyt*(flux_y[i,j,ex]-flux_y[i,j-1,ex])*rc(i) + dt*source[i,j,ex]\n",
    "            Qout[i,j,ey] = Qin[i,j,ey]*rc(i) - dxt*(flux_x[i,j,ey]*rbp-flux_x[i-1,j,ey]*rbm) - dyt*(flux_y[i,j,ey]-flux_y[i,j-1,ey])*rc(i) + dt*source[i,j,ey]\n",
    "            Qout[i,j,ez] = Qin[i,j,ez]*rc(i) - dxt*(flux_x[i,j,ez]*rbp-flux_x[i-1,j,ez]*rbm) - dyt*(flux_y[i,j,ez]-flux_y[i,j-1,ez])*rc(i) + dt*source[i,j,ez]\n",
    "\n",
    "            Qout[i,j,ne] = Qin[i,j,ne]*rc(i) - dxt*(flux_x[i,j,ne]*rbp-flux_x[i-1,j,ne]*rbm) - dyt*(flux_y[i,j,ne]-flux_y[i,j-1,ne])*rc(i) + dt*source[i,j,ne]\n",
    "            Qout[i,j,jx] = Qin[i,j,jx]*rc(i) - dxt*(flux_x[i,j,jx]*rbp-flux_x[i-1,j,jx]*rbm) - dyt*(flux_y[i,j,jx]-flux_y[i,j-1,jx])*rc(i) + dt*source[i,j,jx] + fac*dxt*(flux_x[i,j,ep]-flux_x[i-1,j,ep])*rc(i)   \n",
    "            Qout[i,j,jy] = Qin[i,j,jy]*rc(i) - dxt*(flux_x[i,j,jy]*rbp-flux_x[i-1,j,jy]*rbm) - dyt*(flux_y[i,j,jy]-flux_y[i,j-1,jy])*rc(i) + dt*source[i,j,jy] + fac*dyt*(flux_y[i,j,ep]-flux_y[i,j-1,ep])*rc(i)\n",
    "            Qout[i,j,jz] = Qin[i,j,jz]*rc(i) - dxt*(flux_x[i,j,jz]*rbp-flux_x[i-1,j,jz]*rbm) - dyt*(flux_y[i,j,jz]-flux_y[i,j-1,jz])*rc(i) + dt*source[i,j,jz]\n",
    "\n",
    "            Qout[i,j,et] = Qin[i,j,et]*rc(i) - dxt*(flux_x[i,j,et]*rbp-flux_x[i-1,j,et]*rbm) - dyt*(flux_y[i,j,et]-flux_y[i,j-1,et])*rc(i) + dt*source[i,j,et]\n",
    "\n",
    "            Qout[i,j,rh:ne+1] = Qout[i,j,rh:ne+1]*rci\n",
    "    return Qout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "After advancing the current using Eq. $(5.24)$ we compute $\\vec E$ and $\\vec j$ from Eq. $(5.25)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def implicit_source2(Qin,flux_x,flux_y):\n",
    "    fac = 1./(mime + Z)     \n",
    "    Ainv=np.zeros((3,3))\n",
    "    mb = ecyc*dt\n",
    "    mb2 = mb**2\n",
    "\n",
    "    for j in range(ngu, ny-ngu):\n",
    "        for i in range(ngu, nx-ngu):\n",
    "\n",
    "            dne = Qin[i,j,ne]\n",
    "\n",
    "            zero = 1.\n",
    "            if(dne < Z*rh_floor*rh_mult):\n",
    "                zero = 0.\n",
    "\n",
    "            dnii = 1./Qin[i,j,rh]\n",
    "            ux = Qin[i,j,mx]*dnii\n",
    "            uy = Qin[i,j,my]*dnii\n",
    "            uz = Qin[i,j,mz]*dnii \n",
    "\n",
    "            hx = Qin[i,j,bx]\n",
    "            hy = Qin[i,j,by]\n",
    "            hz = Qin[i,j,bz]\n",
    "\n",
    "            ma = 1. + dt*gma2*dne*(Qin[i,j,etax] + dt*cfva2)\n",
    "            ma2 = ma**2\n",
    "            mamb = ma*mb\n",
    "\n",
    "            denom = zero/(ma*(ma2 + mb2*(hx**2 + hy**2 + hz**2)))         \n",
    "\n",
    "            Ainv[0,0] = denom*(ma2 + mb2*hx**2)\n",
    "            Ainv[1,1] = denom*(ma2 + mb2*hy**2)\n",
    "            Ainv[2,2] = denom*(ma2 + mb2*hz**2)\n",
    "            Ainv[0,1] = denom*(mb2*hx*hy - mamb*hz)\n",
    "            Ainv[1,2] = denom*(mb2*hy*hz - mamb*hx)\n",
    "            Ainv[2,0] = denom*(mb2*hz*hx - mamb*hy)  \n",
    "            Ainv[1,0] = denom*(mb2*hx*hy + mamb*hz)\n",
    "            Ainv[2,1] = denom*(mb2*hy*hz + mamb*hx)\n",
    "            Ainv[0,2] = denom*(mb2*hz*hx + mamb*hy) \n",
    "\n",
    "            Qjx = Qin[i,j,jx] + dt*gma2*(dne*(Qin[i,j,ex] + uy*hz - uz*hy) + dxi*lil0*(flux_x[i,j,ep] - flux_x[i-1,j,ep]))\n",
    "            Qjy = Qin[i,j,jy] + dt*gma2*(dne*(Qin[i,j,ey] + uz*hx - ux*hz) + dyi*lil0*(flux_y[i,j,ep] - flux_y[i,j-1,ep])) \n",
    "            Qjz = Qin[i,j,jz] + dt*gma2*(dne*(Qin[i,j,ez] + ux*hy - uy*hx))      \n",
    "\n",
    "            Qin[i,j,jx] = Ainv[0,0]*Qjx + Ainv[0,1]*Qjy + Ainv[0,2]*Qjz\n",
    "            Qin[i,j,jy] = Ainv[1,0]*Qjx + Ainv[1,1]*Qjy + Ainv[1,2]*Qjz \n",
    "            Qin[i,j,jz] = Ainv[2,0]*Qjx + Ainv[2,1]*Qjy + Ainv[2,2]*Qjz\n",
    "\n",
    "            Qin[i,j,ex:ez] -= dt*cfva2*Qin[i,j,jx:jz]        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We now write the function that computes the flux `flx` and the freezing speeds `cfx` along the x-direction. These speeds correspond to the largest speed found in the hyperbolic equation at the given location $(x,y)$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def calc_flux_x(Qx):\n",
    "    cfx=np.zeros((nx,nQ))\n",
    "    flx=np.zeros((nx,nQ))\n",
    "    fac = 1./(mime + Z)\n",
    "\n",
    "    for i in range(0, nx):\n",
    "\n",
    "        dni = Qx[i,rh]\n",
    "        #dnii =0\n",
    "        #if (dni>0):\n",
    "        dnii = 1./dni\n",
    "        vx = Qx[i,mx]*dnii \n",
    "        vy = Qx[i,my]*dnii\n",
    "        vz = Qx[i,mz]*dnii\n",
    "\n",
    "        dne = Qx[i,ne]\n",
    "        #dnei =0\n",
    "        #if (dne>0):\n",
    "        dnei = 1./dne\n",
    "        vex = vx - lil0*Qx[i,jx]*dnei\n",
    "        vey = vy - lil0*Qx[i,jy]*dnei\n",
    "        vez = vz - lil0*Qx[i,jz]*dnei\n",
    "\n",
    "        hx = Qx[i,bx]\n",
    "        hy = Qx[i,by]\n",
    "        hz = Qx[i,bz]\n",
    "\n",
    "        Pri = (aindex - 1)*(Qx[i,en] - 0.5*dni*(vx**2 + vy**2 + vz**2))\n",
    "        if(Pri < P_floor):\n",
    "            Pri = P_floor\n",
    "\n",
    "        Pre = (aindex - 1)*(Qx[i,et] - 0.5*memi*dne*(vex**2 + vey**2 + vez**2))  \n",
    "        if(Pre < Z*P_floor):\n",
    "            Pre = Z*P_floor\n",
    "\n",
    "        P = Pri + Pre\n",
    "\n",
    "        vx = (mime*vx + Z*vex)*fac\n",
    "        vy = (mime*vy + Z*vey)*fac\n",
    "        vz = (mime*vz + Z*vez)*fac\n",
    "\n",
    "        flx[i,rh] = Qx[i,mx]\n",
    "        flx[i,mx] = Qx[i,mx]*vx + P            \n",
    "        flx[i,my] = Qx[i,my]*vx                       \n",
    "        flx[i,mz] = Qx[i,mz]*vx                                     \n",
    "        flx[i,en] = (Qx[i,en] + Pri)*vx  \n",
    "\n",
    "        flx[i,bx] =  0.0\n",
    "        flx[i,by] = -Qx[i,ez] \n",
    "        flx[i,bz] =  Qx[i,ey] \n",
    "\n",
    "        flx[i,ex] = 0.\n",
    "        flx[i,ey] = cfva2*hz\n",
    "        flx[i,ez] = -cfva2*hy \n",
    "\n",
    "        flx[i,ne] = dne*vex\n",
    "        flx[i,jx] = vx*Qx[i,jx] + Qx[i,jx]*vx - lil0*dnei*Qx[i,jx]*Qx[i,jx]\n",
    "        flx[i,jy] = vx*Qx[i,jy] + Qx[i,jx]*vy - lil0*dnei*Qx[i,jx]*Qx[i,jy]   \n",
    "        flx[i,jz] = vx*Qx[i,jz] + Qx[i,jx]*vz - lil0*dnei*Qx[i,jx]*Qx[i,jz]    \n",
    "        flx[i,et] = (Qx[i,et] + Pre)*vex\n",
    "        flx[i,ep] = Pre\n",
    "\n",
    "        asqr = sqrt(aindex*P*dnii)\n",
    "        # \tvf1 = abs(vx) + asqr                 \n",
    "        vf1 = sqrt(vx**2 + hz**2*dnii + aindex*P*dnii)\n",
    "        vf2 = clt\n",
    "        vf3 = abs(vex) + asqr\n",
    "\n",
    "        cfx[i,rh] = vf1  \n",
    "        cfx[i,mx] = vf1 \n",
    "        cfx[i,my] = vf1    \n",
    "        cfx[i,mz] = vf1  \n",
    "        cfx[i,en] = vf1 \n",
    "        cfx[i,ne] = vf3 \n",
    "        cfx[i,jx] = vf3 \n",
    "        cfx[i,jy] = vf3 \n",
    "        cfx[i,jz] = vf3 \n",
    "        cfx[i,et] = vf3 \n",
    "        cfx[i,bx] = vf2  \n",
    "        cfx[i,by] = vf2\n",
    "        cfx[i,bz] = vf2\n",
    "        cfx[i,ex] = vf2\n",
    "        cfx[i,ey] = vf2\n",
    "        cfx[i,ez] = vf2\n",
    "    return cfx,flx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We now write the function that computes the flux `fly` and the freezing speeds `cfy` along the y-direction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def calc_flux_y(Qy):\n",
    "    cfy=np.zeros((ny,nQ))\n",
    "    fly=np.zeros((ny,nQ))\n",
    "    fac = 1./(mime + Z)    \n",
    "\n",
    "    for j in range(0, ny):\n",
    "\n",
    "        dni = Qy[j,rh]\n",
    "        #dnii =0\n",
    "        #if (dni>0):\n",
    "        dnii = 1./dni\n",
    "        vx = Qy[j,mx]*dnii \n",
    "        vy = Qy[j,my]*dnii \n",
    "        vz = Qy[j,mz]*dnii \n",
    "        \n",
    "        dne = Qy[j,ne]\n",
    "        #dnei =0\n",
    "        #if (dne>0):\n",
    "        dnei = 1./dne\n",
    "        vex = vx - lil0*Qy[j,jx]*dnei\n",
    "        vey = vy - lil0*Qy[j,jy]*dnei\n",
    "        vez = vz - lil0*Qy[j,jz]*dnei\n",
    "\n",
    "        hx = Qy[j,bx]\n",
    "        hy = Qy[j,by]\n",
    "        hz = Qy[j,bz]\n",
    "\n",
    "        Pri = (aindex - 1)*(Qy[j,en] - 0.5*dni*(vx**2 + vy**2 + vz**2))  \n",
    "        if(Pri < P_floor):\n",
    "            Pri = P_floor\n",
    "\n",
    "        Pre = (aindex - 1)*(Qy[j,et] - 0.5*memi*dne*(vex**2 + vey**2 + vez**2))  \n",
    "        if(Pre < Z*P_floor):\n",
    "            Pre = Z*P_floor\n",
    "\n",
    "        P = Pri + Pre\n",
    "        vx = (mime*vx + Z*vex)*fac\n",
    "        vy = (mime*vy + Z*vey)*fac\n",
    "        vz = (mime*vz + Z*vez)*fac\n",
    "        ## end if\n",
    "\n",
    "        fly[j,rh] = Qy[j,my]\n",
    "        fly[j,mx] = Qy[j,mx]*vy                 \n",
    "        fly[j,my] = Qy[j,my]*vy + P              \n",
    "        fly[j,mz] = Qy[j,mz]*vy     \n",
    "        fly[j,en] = (Qy[j,en] + Pri)*vy\n",
    "\n",
    "        fly[j,bx] =  Qy[j,ez] \n",
    "        fly[j,by] =  0.0\n",
    "        fly[j,bz] = -Qy[j,ex]\n",
    "        fly[j,ex] = -cfva2*hz\n",
    "        fly[j,ey] = 0.\n",
    "        fly[j,ez] =  cfva2*hx\n",
    "\n",
    "        fly[j,ne] = dne*vey\n",
    "        fly[j,jx] = vy*Qy[j,jx] + Qy[j,jy]*vx - lil0*dnei*Qy[j,jy]*Qy[j,jx]  \n",
    "        fly[j,jy] = vy*Qy[j,jy] + Qy[j,jy]*vy - lil0*dnei*Qy[j,jy]*Qy[j,jy] \n",
    "        fly[j,jz] = vy*Qy[j,jz] + Qy[j,jy]*vz - lil0*dnei*Qy[j,jy]*Qy[j,jz]   \n",
    "        fly[j,et] = (Qy[j,et] + Pre)*vey\n",
    "        fly[j,ep] = Pre\n",
    "\n",
    "        asqr = sqrt(aindex*P*dnii)\n",
    "        # vf1 = abs(vy) + asqr               \n",
    "        vf1 = sqrt(vy**2 + hz**2*dnii + aindex*P*dnii)\n",
    "        vf2 = clt\n",
    "        vf3 = abs(vey) + asqr\n",
    "\n",
    "        cfy[j,rh] = vf1 \n",
    "        cfy[j,mx] = vf1  \n",
    "        cfy[j,my] = vf1  \n",
    "        cfy[j,mz] = vf1   \n",
    "        cfy[j,en] = vf1  \n",
    "        cfy[j,ne] = vf3   \n",
    "        cfy[j,jx] = vf3 \n",
    "        cfy[j,jy] = vf3 \n",
    "        cfy[j,jz] = vf3 \n",
    "        cfy[j,et] = vf3 \n",
    "        cfy[j,bx] = vf2\n",
    "        cfy[j,by] = vf2\n",
    "        cfy[j,bz] = vf2\n",
    "        cfy[j,ex] = vf2\n",
    "        cfy[j,ey] = vf2\n",
    "        cfy[j,ez] = vf2\n",
    "    return cfy,fly\n",
    "\n",
    "    # # end def calc_flux_y  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We now smooth out the different fluxes using a total diminishing variation scheme as done in Ch04 but for all variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def tvd2(Qin,n,ff,cfr):   \n",
    "    sl = 1 #use 0.75 for first order\n",
    "    wr = cfr*Qin + ff\n",
    "    wl = cfr*Qin - ff\n",
    "    \n",
    "    fr = wr\n",
    "    fl = np.roll(wl,-1,axis=0)   \n",
    "    \n",
    "    dfrp = np.roll(fr,-1,axis=0) - fr\n",
    "    dfrm = fr - np.roll(fr,+1,axis=0)\n",
    "    dflp = fl - np.roll(fl,-1,axis=0)\n",
    "    dflm = np.roll(fl,+1,axis=0) - fl \n",
    "    dfr=np.zeros((n,nQ))        \n",
    "    dfl=np.zeros((n,nQ))\n",
    "    \n",
    "    flux2 = tvd2_cycle(nQ, n, dfrp, dfrm, dflp, dflm, dfr, dfl, fr, fl, sl)\n",
    "    return flux2\n",
    "\n",
    "\n",
    "@jit(nopython=True)\n",
    "def tvd2_cycle(nQ, n, dfrp, dfrm, dflp, dflm, dfr, dfl, fr, fl, sl):\n",
    "    flux2=np.zeros((n,nQ))\n",
    "    for l in range(nQ):\n",
    "        for i in range(n):\n",
    "            if(dfrp[i,l]*dfrm[i,l] > 0) : \n",
    "                dfr[i,l] = dfrp[i,l]*dfrm[i,l]/(dfrp[i,l] + dfrm[i,l])\n",
    "            else:\n",
    "                dfr[i,l] = 0\n",
    "            if(dflp[i,l]*dflm[i,l] > 0) :\n",
    "                dfl[i,l] = dflp[i,l]*dflm[i,l]/(dflp[i,l] + dflm[i,l])\n",
    "            else:\n",
    "                dfl[i,l] = 0\n",
    "    flux2 = 0.5*(fr - fl + sl*(dfr - dfl))\n",
    "    return flux2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We truly compute all the fluxes here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def get_flux(Qin):\n",
    "    flux_x=np.zeros((nx,ny,nQ))\n",
    "    flux_y=np.zeros((nx,ny,nQ))\n",
    "    fx=np.zeros((nx,nQ))\n",
    "    cfx=np.zeros((nx,nQ))\n",
    "    ffx=np.zeros((nx,nQ))\n",
    "    fy=np.zeros((ny,nQ))\n",
    "    cfy=np.zeros((ny,nQ))\n",
    "    ffy=np.zeros((ny,nQ))\n",
    "    for j  in range(0, ny):\n",
    "        cfx,ffx=calc_flux_x(Qin[:,j,:])           \n",
    "        flux_x[:,j,:]=tvd2(Qin[:,j,:],nx,ffx,cfx)\n",
    "    for i  in range(0, nx):\n",
    "        cfy,ffy=calc_flux_y( Qin[i,:,:])       \n",
    "        flux_y[i,:,:]=tvd2(Qin[i,:,:],ny,ffy,cfy)\n",
    "    return flux_x,flux_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We now limit flows to avoid minor instabilities caused by round errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def limit_flow(Qin):\n",
    "\n",
    "    en_floor = rh_floor*T_floor/(aindex-1)      \n",
    "\n",
    "    for j in range(ngu-1, ny-ngu+1):\n",
    "        for i in range(ngu-1, nx-ngu+1):\n",
    "\n",
    "            if(Qin[i,j,rh]  <=  rh_floor  or  Qin[i,j,ne]  <=  Z*rh_floor):\n",
    "                Qin[i,j,rh] = rh_floor\n",
    "                Qin[i,j,ne] = Z*rh_floor\n",
    "                Qin[i,j,mx] = 0.0\n",
    "                Qin[i,j,my] = 0.0\n",
    "                Qin[i,j,mz] = 0.0\n",
    "                Qin[i,j,en] = en_floor\n",
    "                Qin[i,j,et] = Z*en_floor\n",
    "\n",
    "            if(abs(Qin[i,j,jx]) > vhcf*Qin[i,j,rh]):\n",
    "                sign=1\n",
    "                if (Qin[i,j,jx]<0):\n",
    "                    sign=-1;\n",
    "                Qin[i,j,jx] = vhcf*Qin[i,j,rh]*sign\n",
    "            if(abs(Qin[i,j,jy]) > vhcf*Qin[i,j,rh]):\n",
    "                sign=1\n",
    "                if (Qin[i,j,jy]<0):\n",
    "                    sign=-1;\n",
    "                Qin[i,j,jy] = vhcf*Qin[i,j,rh]*sign\n",
    "            if(abs(Qin[i,j,jz]) > vhcf*Qin[i,j,rh]):\n",
    "                sign=1\n",
    "                if (Qin[i,j,jz]<0):\n",
    "                    sign=-1;\n",
    "                Qin[i,j,jz] = vhcf*Qin[i,j,rh]*sign\n",
    "\n",
    "            if(abs(Qin[i,j,mx]) > clt*Qin[i,j,rh]):\n",
    "                sign=1\n",
    "                if (Qin[i,j,mx]<0):\n",
    "                    sign=-1;\n",
    "                Qin[i,j,mx] = clt*Qin[i,j,rh]*sign\n",
    "            if(abs(Qin[i,j,my]) > clt*Qin[i,j,rh]):\n",
    "                sign=1\n",
    "                if (Qin[i,j,my]<0):\n",
    "                    sign=-1;\n",
    "                Qin[i,j,my] = clt*Qin[i,j,rh]*sign\n",
    "            if(abs(Qin[i,j,mz]) > clt*Qin[i,j,rh]):\n",
    "                sign=1\n",
    "                if (Qin[i,j,mz]<0):\n",
    "                    sign=-1;\n",
    "                Qin[i,j,mz] = clt*Qin[i,j,rh]*sign\n",
    "\n",
    "            if(MMask[i,j]==1):\n",
    "                Qin[i,j,mx] = 0.0\n",
    "                Qin[i,j,my] = 0.0\n",
    "                Qin[i,j,mz] = 0.0 \n",
    "                Qin[i,j,en] = en_floor\n",
    "                Qin[i,j,et] = Z*en_floor        \n",
    "\n",
    "            if(BMask[i,j]==1):\n",
    "                Qin[i,j,bz] = 0.0         \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Domain and variable definitions\n",
    "Everything you find below should be the only lines of code you need to change.\n",
    "We now initialize the computational domain. `ngu` correspond to the number of points at the boundary. Leave it alone. `nx` and `ny` are the number of grid points along the x- and y-directions. `lxu` and `lxd` are the upper and lower corners of the mesh along the x-direction. `lyu` and `lyd` are the corners for the y-direction. `dxi` and `dyi` are the inverse of the grid resolution `dx` and `dy`. Since we use both `1/dx` and `/dy` a lot and it is more expansive to compute inverse, we compute them once and that's it. Since python is interactive, we could see them as constants here.\n",
    "\n",
    "**Every time you want to restart a new computation you need to recompile what's below.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "ngu=2\n",
    "nx=35\n",
    "ny=40\n",
    "\n",
    "lxu=15e-3/L0\n",
    "lxd=0\n",
    "\n",
    "lyd=0\n",
    "lyu = lyd + (ny-2*ngu)*(lxu-lxd)/(nx-2*ngu)  \n",
    "\n",
    "dxi = (nx-2*ngu)/(lxu-lxd)\n",
    "dyi = (ny-2*ngu)/(lyu-lyd)\n",
    "loc_lxd = lxd \n",
    "loc_lyd = lyd\n",
    "box=np.array([lxd,lxu,lyd,lyu])*L0/1e-3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We now compute the maximum size of the time step using the $CFL$ condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def get_min_dt(Q):\n",
    "    valf = 0.\n",
    "    vmag = 0.\n",
    "\n",
    "    for j in range(ny):\n",
    "        for i in range(nx):\n",
    "\n",
    "            valf0 = sqrt((Q[i,j,bx]**2 + Q[i,j,by]**2 + Q[i,j,bz]**2)/Q[i,j,rh])\n",
    "            if(valf0 > valf  and  Q[i,j,rh] > rh_mult*rh_floor):\n",
    "                valf = valf0\n",
    "\n",
    "            vmag0 = sqrt((Q[i,j,mx]**2 + Q[i,j,my]**2))/Q[i,j,rh]\n",
    "            if(vmag0 > vmag  and  Q[i,j,rh] > rh_mult*rh_floor):\n",
    "                vmag = vmag0\n",
    "    cfl = 0.5*(1. - 0.6*(valf/clt)**2/(1. + (valf/clt)**2))\n",
    "    vmax = max(clt,vmag)\n",
    "    dt_min = cfl/(dxi*vmax)  \n",
    "    return dt_min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Below we defined the initial `ti` and final `tf` times. `n_out` corresponds to to the total number of outputs. The rest are variable initialization. Note the most important one, the time `t`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "ti = 00.0E-2\n",
    "tf = 150.e-9/t0\n",
    "tr = 150e-9/t0\n",
    "tdp = 6*tr\n",
    "t = ti\n",
    "dt = cflm/(2.828*dxi*clt)    \n",
    "n_steps=0\n",
    "n_out=10\n",
    "t_count=0\n",
    "Ipeak=1e6/(J0*L0*L0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "And now all our arrays are defined here. Note that we use a mask `MMask` for parts of the simulation space were we want to preclude motion. That will come handy later in the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "Q=np.zeros((nx,ny,nQ))\n",
    "MMask=np.zeros((nx,ny))\n",
    "BMask=np.zeros((nx,ny))\n",
    "Q1=np.copy(Q)\n",
    "Q2=np.copy(Q)\n",
    "sources=np.zeros((nx,ny,nQ))\n",
    "flux_x=np.zeros((nx,ny,nQ))\n",
    "flux_y=np.zeros((nx,ny,nQ))\n",
    "ect=np.zeros((nx,ny))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "###  Initial conditions\n",
    "That's for an empty domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "Q[:,:,rh]=rh_floor\n",
    "Q[:,:,en]=0.026/te0*Q[:,:,rh]*(aindex-1)\n",
    "Q[:,:,ne]=Z*Q[:,:,rh]\n",
    "Q[:,:,et]=Z*Q[:,:,en]\n",
    "\n",
    "K_rad=2e-3/L0\n",
    "A_rad=10e-3/L0\n",
    "height =2e-3/L0\n",
    "\n",
    "for i in range (nx):\n",
    "    for j in range (ny):\n",
    "        if (yc(j)<height):\n",
    "            if (xc(i)<K_rad or xc(i)>A_rad):\n",
    "                MMask[i,j]=1\n",
    "                BMask[i,j]=1\n",
    "                Q[i,j,rh]=6e28/n0\n",
    "        if (yc(j)>=height and yc(j+1)<height+2./dxi):\n",
    "            Q[i,j,rh]=6e25/n0\n",
    "        Q[i,j,en]=0.026/te0*Q[i,j,rh]*(aindex-1)\n",
    "        Q[i,j,ne]=Z*Q[i,j,rh]\n",
    "        Q[i,j,et]=Z*Q[i,j,en]\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Boundary conditions\n",
    "Below we define our boundary conditions. In this example, we do not use the time `t` and focus only on steady state solutions. The first block define the default boundary conditions for the whole domain. Note that the conditions for the y-direction are pretty simple. These boundary conditions correspond to an open boundary. everything can go through it.\n",
    "\n",
    "Boundary conditions are a bit more complex for the x-direction. We are using a cylindrical coordinate system with open boundary conditions on the right but reflecting boundary conditions on the left. That's because nothing can pass through the axis of symmetry $r=0$.\n",
    "\n",
    "At the end we have the boundary conditions that make up our problem, e.g. material free falling toward the axis of our coordinate system. Note that we are using very large density because the author works in high energy density plasmas. the initial parameters `L0`, `t0` and `n0` can be changed to accommodate other regimes. But, chances are that the CFL scaler `cflm` may have to be adjusted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def set_bc(Qin,t):\n",
    "    Qin[nx-ngu,:,:] = Qin[nx-ngu-1,:,:]*xc(nx-ngu-1)/xc(nx-ngu)\n",
    "    Qin[nx-ngu+1,:,:] = Qin[nx-ngu,:,:]*xc(nx-ngu)/xc(nx-ngu+1)\n",
    "    Qin[:,1,:] = Qin[:,2,:]\n",
    "    Qin[:,0,:] = Qin[:,1,:]\n",
    "    Qin[:,ny-2,:] = Qin[:,ny-3,:]\n",
    "    Qin[:,ny-1,:] = Qin[:,ny-2,:]\n",
    "    \n",
    "    for l in range(ngu):\n",
    "        for k in range(nQ):\n",
    "            if(k == mx or k == jx or k == ex or k == bx):\n",
    "                Qin[l,:,k] = -Qin[2*ngu-l-1,:,k]\n",
    "            if (k == mz or k == jz or k == ez or k == bz) :\n",
    "                Qin[l,:,k] = -Qin[2*ngu-l-1,:,k]\n",
    "            if (k == my or k == jy or k == ey or k == by) :\n",
    "                Qin[l,:,k] = Qin[2*ngu-l-1,:,k]\n",
    "            if (k == rh or k == ne or k == en or k == et) :\n",
    "                Qin[l,:,k] = Qin[2*ngu-l-1,:,k]\n",
    "    \n",
    "    #our boundary condtions for injecting material radially into our domain\n",
    "    #note that all the values used are dimensionless\n",
    "    N=3\n",
    "    for i in range (nx):\n",
    "        for j in range (ngu):\n",
    "            if (xc(i)>K_rad and xc(i)<A_rad):\n",
    "                Qin[i,j,bz]=Icur(t)/(2*np.pi*xc(nx-1-k))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Computation and outputs\n",
    "Let's define some plotting parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "matplotlib.rcParams.update({'font.size': 22})\n",
    "xi = np.linspace(lxd*L0, lxu*L0, nx-2*ngu-1)\n",
    "yi = np.linspace(lyd*L0, lyu*L0, ny-2*ngu-1)\n",
    "progress_bar = FloatProgress(min=ti, max=tf,description='Progress') \n",
    "output_bar = FloatProgress(min=0, max=(tf-ti)/n_out,description='Next output') \n",
    "columns = 2\n",
    "rows = 4\n",
    "box=np.array([lxd,lxu,lyd,lyu])*L0/1e-3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Here we go again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "35084441d6bd48539492524df6845b76",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=1.3534535026416965, description='Progress', max=1.5)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "FloatProgress(value=0.0, description='Next output', max=0.15)"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1900 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "xi = np.linspace(lxd*L0, lxu*L0, nx-2*ngu-1)\n",
    "yi = np.linspace(lyd*L0, lyu*L0, ny-2*ngu-1)\n",
    "while t<tf:\n",
    "    dt=get_min_dt(Q)\n",
    "\n",
    "    set_bc(Q,t)\n",
    "    sources=get_sources(Q)\n",
    "    flux_x,flux_y=get_flux(Q)\n",
    "    Q1=advance_time_level(Q,ect,flux_x,flux_y,sources)\n",
    "    implicit_source2(Q1,flux_x,flux_y)\n",
    "    limit_flow(Q1)\n",
    "    \n",
    "    set_bc(Q1,t+dt)\n",
    "    sources=get_sources(Q1)\n",
    "    flux_x,flux_y=get_flux(Q1)\n",
    "    Q2=advance_time_level(Q1,ect,flux_x,flux_y,sources)\n",
    "    implicit_source2(Q2,flux_x,flux_y)\n",
    "    limit_flow(Q2)\n",
    "    Q=0.5*(Q+Q2)\n",
    "    limit_flow(Q)\n",
    "    \n",
    "    n_steps+=1 #time step increased by 1\n",
    "    t_count+=dt #and the time by dt\n",
    "    \n",
    "    progress_bar.value=t #we have defined some progress bars cause python is slow\n",
    "    output_bar.value+=dt # we need to monitor our progress and reduce resolution if it takes too long\n",
    "    \n",
    "    if ((t==ti) or (t_count>(tf-ti)/n_out)): #everything below is plotting....\n",
    "        fig=plt.figure(figsize=(20, 19))\n",
    "        for i in range(1, rows+1):\n",
    "            for j in range(1, columns+1):\n",
    "                k=j+(i-1)*columns\n",
    "                fig.add_subplot(rows, columns, k)\n",
    "                if (k==1):\n",
    "                    data= np.flip(np.transpose(Q[ngu:nx-ngu-1,ngu:ny-ngu-1,rh]*n0),0)\n",
    "                    im = plt.imshow(data, cmap=plt.cm.nipy_spectral, norm=colors.LogNorm(vmin=data.min(), vmax=data.max()),extent=box)\n",
    "                    cb = fig.colorbar(im,fraction=0.046, pad=0.04)\n",
    "                    cb.ax.set_ylabel('$\\log_{10}n_i(m^{-3})$', rotation=-90)\n",
    "                if (k==3):\n",
    "                    data=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,mx]**2\n",
    "                    data+=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,my]**2\n",
    "                    data+=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,mz]**2\n",
    "                    data/=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,rh]**2\n",
    "                    data=np.sqrt(data)*L0/t0\n",
    "                    data= np.flip(np.transpose(data),0)\n",
    "                    im = plt.imshow(data, extent=box)\n",
    "                    cb = fig.colorbar(im,fraction=0.046, pad=0.04)\n",
    "                    cb.ax.set_ylabel('$u(m/s)$', rotation=-90)\n",
    "                if (k==2):\n",
    "                    data=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,mx]**2\n",
    "                    data/=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,rh]**2\n",
    "                    data=np.sqrt(data)*L0/t0\n",
    "                    data= np.flip(np.transpose(data),0)\n",
    "                    im = plt.imshow(data, extent=box)\n",
    "                    cb = fig.colorbar(im,fraction=0.046, pad=0.04)\n",
    "                    cb.ax.set_ylabel('$u_r(m/s)$', rotation=-90)\n",
    "                if (k==4):\n",
    "                    data=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,my]**2\n",
    "                    data/=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,rh]**2\n",
    "                    data=np.sqrt(data)*L0/t0\n",
    "                    data= np.flip(np.transpose(data),0)\n",
    "                    im = plt.imshow(data, extent=box)\n",
    "                    cb = fig.colorbar(im,fraction=0.046, pad=0.04)\n",
    "                    cb.ax.set_ylabel('$u_z(m/s)$', rotation=-90)\n",
    "                if (k==6):\n",
    "                    data=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,mz]**2\n",
    "                    data/=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,rh]**2\n",
    "                    data=np.sqrt(data)*L0/t0\n",
    "                    data= np.flip(np.transpose(data),0)\n",
    "                    im = plt.imshow(data, extent=box)\n",
    "                    cb = fig.colorbar(im,fraction=0.046, pad=0.04)\n",
    "                    cb.ax.set_ylabel('$u_\\phi(m/s)$', rotation=-90)\n",
    "                if (k==5):\n",
    "                    data=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,tpi]*te0\n",
    "                    data= np.flip(np.transpose(data),0)\n",
    "                    im = plt.imshow(data, cmap=plt.cm.jet,  extent=box)\n",
    "                    cb = fig.colorbar(im,fraction=0.046, pad=0.04)\n",
    "                    cb.ax.set_ylabel('$T(eV)$', rotation=-90)\n",
    "                if (k==7):\n",
    "                    data=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,jx]**2+Q[ngu:nx-ngu-1,ngu:ny-ngu-1,jy]**2+Q[ngu:nx-ngu-1,ngu:ny-ngu-1,jz]**2\n",
    "                    data=np.sqrt(data)*J0\n",
    "                    data= np.flip(np.transpose(data),0)\n",
    "                    im = plt.imshow(data, extent=box)\n",
    "                    cb = fig.colorbar(im,fraction=0.046, pad=0.04)\n",
    "                    cb.ax.set_ylabel('$J(A/m^2)$', rotation=-90)\n",
    "                if (k==8):\n",
    "                    data=Q[ngu:nx-ngu-1,ngu:ny-ngu-1,bx]**2+Q[ngu:nx-ngu-1,ngu:ny-ngu-1,by]**2+Q[ngu:nx-ngu-1,ngu:ny-ngu-1,bz]**2\n",
    "                    data=np.sqrt(data)*B0\n",
    "                    data= np.flip(np.transpose(data),0)\n",
    "                    im = plt.imshow(data, extent=box)\n",
    "                    cb = fig.colorbar(im,fraction=0.046, pad=0.04)\n",
    "                    cb.ax.set_ylabel('$B(T)$', rotation=-90)\n",
    "                im.set_interpolation('quadric')\n",
    "                cb.ax.yaxis.set_label_coords(9, 0.5)\n",
    "                plt.gca().axes.get_yaxis().set_visible(False)\n",
    "                plt.gca().axes.get_xaxis().set_visible(False)\n",
    "                if (j==1):\n",
    "                    plt.ylabel('z(mm)', rotation=90)\n",
    "                    plt.gca().axes.get_yaxis().set_visible(True)\n",
    "                if (i==rows):\n",
    "                    plt.xlabel('r(mm)', rotation=0)\n",
    "                    plt.gca().axes.get_xaxis().set_visible(True)\n",
    "        plt.tight_layout()\n",
    "        clear_output()\n",
    "        output_bar.value=0\n",
    "        display(progress_bar) # display the bar\n",
    "        display(output_bar) # display the bar\n",
    "        t_count=0\n",
    "        show_inline_matplotlib_plots()\n",
    "    t=t+dt\n",
    "\n",
    "#we are now done. Let's turn off the progress bars.    \n",
    "output_bar.close()\n",
    "del output_bar\n",
    "progress_bar.close()\n",
    "del progress_bar\n",
    "\n",
    "print(\"DONE\")"
   ]
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   "source": [
    "Â© Pierre Gourdain, The University of Rochester, Charlie Seyler, Cornell University, 2020"
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